Bibliographic record
Abstract
Recent research shows that the judged likelihood of properties of modified nouns (baby ducks have webbed feet) is reduced relative to judgments for unmodified nouns (ducks have webbed feet). This modification effect has been taken as evidence both for and against the idea that combined concepts automatically inherit properties from their constituent concepts. Experiments 1 and 2 replicate this effect and demonstrate a reversed modification effect with false properties. That is, false properties are judged more likely with modification (e.g., purple candles have teeth is judged more likely than candles have teeth). These experiments also show that properties that are neither generically true nor false are unaffected by modification. Experiments 3 and 4 manipulate participants' expectation of contrast by showing modified and unmodified nouns that either match or mismatch in terms of a property and show that the judged likelihood of properties depends on the expectations of contrast set up by the manipulation. These results show that the modification effect is primarily driven by participants' understanding of the relation of subcategories to categories, rather than by the features of the concepts being combined, suggesting that the process of property attribution in combined concepts is strongly affected by pragmatic factors and is less strongly dependent on conceptual content than most theories of conceptual combination would suggest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".